code: def message_and_aggregate(
      self, adj_t: SparseTensor, x: Tensor
  ) -> Tensor:
      deg = adj_t.storage.rowcount().to(x.dtype).view(-1, 1)
      out = 0
      for (aggr, scaler), pre_lin, post_lin in zip(
          product(self.aggregators, self.scalers),
          self.pre_lins,
          self.post_lins,
      ):
          h = pre_lin(x).relu_()
          h = segment_csr(
              h[adj_t.storage.col()],
              adj_t.storage.rowptr(),
              reduce=aggr,
          )
          h = post_lin(h)
          if scaler == "amplification":
              h *= (deg + 1).log() / self.avg_deg["log"]
          elif scaler == "attenuation":
              h *= self.avg_deg["log"] / ((deg + 1).log() + EPS)
  
          out += h
  
      return out
  
imports: from itertools import product
  from typing import Optional, List
  import tqdm
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_sparse import SparseTensor
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import PNAConv
  from torch_scatter import segment_csr
  from ..models import ScalableGNN
label_desc: Use image node_id 4 for calling the PNAConv obj's underlying member method code with example usage: obj.message_and_aggregate(adj_t, x) and returns: out